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IR Based Technique For Linearizing Abstract Method Invocation In Plagiarism Suspected Source Code Pair

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Karnalim, Oscar (Author)
المجلد/العدد: مج31, ع3
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2019
الصفحات: 327 - 334
DOI: 10.33948/0584-031-003-006
ISSN: 1319-1578
رقم MD: 974684
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
علوم الحاسوب | هندسة البرمجيات | استرجاع المعلومات
كلمات المؤلف المفتاحية:
Abstract Method Linearization | Source Code Plagiarism | Software Engineering | Information Retrieva
رابط المحتوى:
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المستخلص: According to several works, low-level approach is an effective and efficient solution for detecting source code plagiarism. Instead of relying on source code tokens, it compares the executable form of given code; that form only contains semantic-preserving tokens and is resistant to various plagiarism attacks. However, to our knowledge, an issue about statically linearizing abstract method (i.e. replacing each abstract method invocation with its respective invoked method content without considering invocation semantic) has not been handled comprehensively. Such issue, at some extent, will generate inaccurate plagiarism detection result when handling object-oriented source codes. This paper aims to solve such issue locally per plagiarism-suspected pair. It will generate all possible linearization pair alternatives and select the correct one through IR-based similarity. According to our evaluation regarding the reversed number of mismatched token, the number of false positive, and the number of process, proposed technique is more effective and efficient when compared to state-of-the-art and combinatoric technique. In addition, it is also observed that each IR mechanism used in proposed technique has its own exclusive benefit for selecting the correct linearized forms. ©2018 The Author. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ISSN: 1319-1578

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